Maintain a read-only CPU state dict copy in CachedModelWithPartialLoad.

docs/contributing/MODEL_MANAGER.md

@@ -1364,7 +1364,6 @@ the in-memory loaded model:
 |----------------|-----------------|------------------|
 | `config`       | AnyModelConfig         | A copy of the model's configuration record for retrieving base type, etc. |
 | `model`        | AnyModel               | The instantiated model (details below) |
-| `locker`       | ModelLockerBase        | A context manager that mediates the movement of the model into VRAM |
 
 ### get_model_by_key(key, [submodel]) -> LoadedModel
 

invokeai/app/api/routers/model_manager.py

@@ -37,7 +37,7 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -20,7 +20,7 @@ from invokeai.app.services.invocation_stats.invocation_stats_common import (
     NodeExecutionStatsSummary,
 )
 from invokeai.app.services.invoker import Invoker
-from invokeai.backend.model_manager.load.model_cache import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 
 # Size of 1GB in bytes.
 GB = 2**30

invokeai/app/services/model_load/model_load_base.py

@@ -7,7 +7,7 @@ from typing import Callable, Optional
 
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
 from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 
 
 class ModelLoadServiceBase(ABC):
@@ -24,7 +24,7 @@ class ModelLoadServiceBase(ABC):
 
     @property
     @abstractmethod
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the RAM cache used by this loader."""
 
     @abstractmethod

invokeai/app/services/model_load/model_load_default.py

@@ -18,7 +18,7 @@ from invokeai.backend.model_manager.load import (
     ModelLoaderRegistry,
     ModelLoaderRegistryBase,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
@@ -30,7 +30,7 @@ class ModelLoadService(ModelLoadServiceBase):
     def __init__(
         self,
         app_config: InvokeAIAppConfig,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
         registry: Optional[Type[ModelLoaderRegistryBase]] = ModelLoaderRegistry,
     ):
         """Initialize the model load service."""
@@ -45,7 +45,7 @@ class ModelLoadService(ModelLoadServiceBase):
         self._invoker = invoker
 
     @property
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the RAM cache used by this loader."""
         return self._ram_cache
 
@@ -78,9 +78,8 @@ class ModelLoadService(ModelLoadServiceBase):
         self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None
     ) -> LoadedModelWithoutConfig:
         cache_key = str(model_path)
-        ram_cache = self.ram_cache
         try:
-            return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))
+            return LoadedModelWithoutConfig(cache_record=self._ram_cache.get(key=cache_key), cache=self._ram_cache)
         except IndexError:
             pass
 
@@ -109,5 +108,5 @@ class ModelLoadService(ModelLoadServiceBase):
         )
         assert loader is not None
         raw_model = loader(model_path)
-        ram_cache.put(key=cache_key, model=raw_model)
-        return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))
+        self._ram_cache.put(key=cache_key, model=raw_model)
+        return LoadedModelWithoutConfig(cache_record=self._ram_cache.get(key=cache_key), cache=self._ram_cache)

invokeai/app/services/model_manager/model_manager_default.py

@@ -16,7 +16,8 @@ from invokeai.app.services.model_load.model_load_base import ModelLoadServiceBas
 from invokeai.app.services.model_load.model_load_default import ModelLoadService
 from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
 from invokeai.app.services.model_records.model_records_base import ModelRecordServiceBase
-from invokeai.backend.model_manager.load import ModelCache, ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 

invokeai/backend/model_manager/load/__init__.py

@@ -8,7 +8,7 @@ from pathlib import Path
 
 from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig, ModelLoaderBase
 from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_cache.model_cache_default import ModelCache
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry, ModelLoaderRegistryBase
 
 # This registers the subclasses that implement loaders of specific model types

invokeai/backend/model_manager/load/load_base.py

@@ -5,7 +5,6 @@ Base class for model loading in InvokeAI.
 
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
-from dataclasses import dataclass
 from logging import Logger
 from pathlib import Path
 from typing import Any, Dict, Generator, Optional, Tuple
@@ -18,19 +17,17 @@ from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 
 
-@dataclass
 class LoadedModelWithoutConfig:
-    """
-    Context manager object that mediates transfer from RAM<->VRAM.
+    """Context manager object that mediates transfer from RAM<->VRAM.
 
     This is a context manager object that has two distinct APIs:
 
     1. Older API (deprecated):
-    Use the LoadedModel object directly as a context manager.
-    It will move the model into VRAM (on CUDA devices), and
+    Use the LoadedModel object directly as a context manager.  It will move the model into VRAM (on CUDA devices), and
     return the model in a form suitable for passing to torch.
     Example:
     ```
@@ -40,13 +37,9 @@ class LoadedModelWithoutConfig:
     ```
 
     2. Newer API (recommended):
-    Call the LoadedModel's `model_on_device()` method in a
-    context. It returns a tuple consisting of a copy of
-    the model's state dict in CPU RAM followed by a copy
-    of the model in VRAM. The state dict is provided to allow
-    LoRAs and other model patchers to return the model to
-    its unpatched state without expensive copy and restore
-    operations.
+    Call the LoadedModel's `model_on_device()` method in a context. It returns a tuple consisting of a copy of the
+    model's state dict in CPU RAM followed by a copy of the model in VRAM. The state dict is provided to allow LoRAs and
+    other model patchers to return the model to its unpatched state without expensive copy and restore operations.
 
     Example:
     ```
@@ -55,43 +48,42 @@ class LoadedModelWithoutConfig:
         image = vae.decode(latents)[0]
     ```
 
-    The state_dict should be treated as a read-only object and
-    never modified. Also be aware that some loadable models do
-    not have a state_dict, in which case this value will be None.
+    The state_dict should be treated as a read-only object and never modified. Also be aware that some loadable models
+    do not have a state_dict, in which case this value will be None.
     """
 
-    _locker: ModelLockerBase
+    def __init__(self, cache_record: CacheRecord, cache: ModelCache):
+        self._cache_record = cache_record
+        self._cache = cache
 
     def __enter__(self) -> AnyModel:
-        """Context entry."""
-        self._locker.lock()
+        self._cache.lock(self._cache_record.key)
         return self.model
 
     def __exit__(self, *args: Any, **kwargs: Any) -> None:
-        """Context exit."""
-        self._locker.unlock()
+        self._cache.unlock(self._cache_record.key)
 
     @contextmanager
     def model_on_device(self) -> Generator[Tuple[Optional[Dict[str, torch.Tensor]], AnyModel], None, None]:
         """Return a tuple consisting of the model's state dict (if it exists) and the locked model on execution device."""
-        locked_model = self._locker.lock()
+        self._cache.lock(self._cache_record.key)
         try:
-            state_dict = self._locker.get_state_dict()
-            yield (state_dict, locked_model)
+            yield (self._cache_record.cached_model.get_cpu_state_dict(), self._cache_record.cached_model.model)
         finally:
-            self._locker.unlock()
+            self._cache.unlock(self._cache_record.key)
 
     @property
     def model(self) -> AnyModel:
         """Return the model without locking it."""
-        return self._locker.model
+        return self._cache_record.cached_model.model
 
 
-@dataclass
 class LoadedModel(LoadedModelWithoutConfig):
     """Context manager object that mediates transfer from RAM<->VRAM."""
 
-    config: Optional[AnyModelConfig] = None
+    def __init__(self, config: Optional[AnyModelConfig], cache_record: CacheRecord, cache: ModelCache):
+        super().__init__(cache_record=cache_record, cache=cache)
+        self.config = config
 
 
 # TODO(MM2):
@@ -110,7 +102,7 @@ class ModelLoaderBase(ABC):
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         pass
@@ -138,6 +130,6 @@ class ModelLoaderBase(ABC):
 
     @property
     @abstractmethod
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the ram cache associated with this loader."""
         pass

invokeai/backend/model_manager/load/load_default.py

@@ -14,7 +14,8 @@ from invokeai.backend.model_manager import (
 )
 from invokeai.backend.model_manager.config import DiffusersConfigBase
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache, get_model_cache_key
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.util.devices import TorchDevice
@@ -28,7 +29,7 @@ class ModelLoader(ModelLoaderBase):
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         self._app_config = app_config
@@ -54,11 +55,11 @@ class ModelLoader(ModelLoaderBase):
             raise InvalidModelConfigException(f"Files for model '{model_config.name}' not found at {model_path}")
 
         with skip_torch_weight_init():
-            locker = self._load_and_cache(model_config, submodel_type)
-        return LoadedModel(config=model_config, _locker=locker)
+            cache_record = self._load_and_cache(model_config, submodel_type)
+        return LoadedModel(config=model_config, cache_record=cache_record, cache=self._ram_cache)
 
     @property
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the ram cache associated with this loader."""
         return self._ram_cache
 
@@ -66,10 +67,10 @@ class ModelLoader(ModelLoaderBase):
         model_base = self._app_config.models_path
         return (model_base / config.path).resolve()
 
-    def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> ModelLockerBase:
+    def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> CacheRecord:
         stats_name = ":".join([config.base, config.type, config.name, (submodel_type or "")])
         try:
-            return self._ram_cache.get(config.key, submodel_type, stats_name=stats_name)
+            return self._ram_cache.get(key=get_model_cache_key(config.key, submodel_type), stats_name=stats_name)
         except IndexError:
             pass
 
@@ -78,16 +79,11 @@ class ModelLoader(ModelLoaderBase):
         loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(
-            config.key,
-            submodel_type=submodel_type,
+            get_model_cache_key(config.key, submodel_type),
             model=loaded_model,
         )
 
-        return self._ram_cache.get(
-            key=config.key,
-            submodel_type=submodel_type,
-            stats_name=stats_name,
-        )
+        return self._ram_cache.get(key=get_model_cache_key(config.key, submodel_type), stats_name=stats_name)
 
     def get_size_fs(
         self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None

invokeai/backend/model_manager/load/model_cache/__init__.py

@@ -1,6 +0,0 @@
-"""Init file for ModelCache."""
-
-from .model_cache_base import ModelCacheBase, CacheStats  # noqa F401
-from .model_cache_default import ModelCache  # noqa F401
-
-_all__ = ["ModelCacheBase", "ModelCache", "CacheStats"]

invokeai/backend/model_manager/load/model_cache/cache_record.py

@@ -0,0 +1,31 @@
+from dataclasses import dataclass
+
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_only_full_load import (
+    CachedModelOnlyFullLoad,
+)
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_with_partial_load import (
+    CachedModelWithPartialLoad,
+)
+
+
+@dataclass
+class CacheRecord:
+    """A class that represents a model in the model cache."""
+
+    # Cache key.
+    key: str
+    # Model in memory.
+    cached_model: CachedModelWithPartialLoad | CachedModelOnlyFullLoad
+    # If locks > 0, the model is actively being used, so we should do our best to keep it on the compute device.
+    _locks: int = 0
+
+    def lock(self) -> None:
+        self._locks += 1
+
+    def unlock(self) -> None:
+        self._locks -= 1
+        assert self._locks >= 0
+
+    @property
+    def is_locked(self) -> bool:
+        return self._locks > 0

invokeai/backend/model_manager/load/model_cache/cache_stats.py

@@ -0,0 +1,15 @@
+from dataclasses import dataclass, field
+from typing import Dict
+
+
+@dataclass
+class CacheStats(object):
+    """Collect statistics on cache performance."""
+
+    hits: int = 0  # cache hits
+    misses: int = 0  # cache misses
+    high_watermark: int = 0  # amount of cache used
+    in_cache: int = 0  # number of models in cache
+    cleared: int = 0  # number of models cleared to make space
+    cache_size: int = 0  # total size of cache
+    loaded_model_sizes: Dict[str, int] = field(default_factory=dict)

invokeai/backend/model_manager/load/model_cache/cached_model/cached_model_only_full_load.py

@@ -0,0 +1,81 @@
+from typing import Any
+
+import torch
+
+
+class CachedModelOnlyFullLoad:
+    """A wrapper around a PyTorch model to handle full loads and unloads between the CPU and the compute device.
+
+    Note: "VRAM" is used throughout this class to refer to the memory on the compute device. It could be CUDA memory,
+    MPS memory, etc.
+    """
+
+    def __init__(self, model: torch.nn.Module | Any, compute_device: torch.device, total_bytes: int):
+        """Initialize a CachedModelOnlyFullLoad.
+
+        Args:
+            model (torch.nn.Module | Any): The model to wrap. Should be on the CPU.
+            compute_device (torch.device): The compute device to move the model to.
+            total_bytes (int): The total size (in bytes) of all the weights in the model.
+        """
+        # model is often a torch.nn.Module, but could be any model type. Throughout this class, we handle both cases.
+        self._model = model
+        self._compute_device = compute_device
+        self._total_bytes = total_bytes
+        self._is_in_vram = False
+
+    @property
+    def model(self) -> torch.nn.Module:
+        return self._model
+
+    def get_cpu_state_dict(self) -> dict[str, torch.Tensor] | None:
+        """Get a read-only copy of the model's state dict in RAM."""
+        # TODO(ryand): Document this better and implement it.
+        return None
+
+    def total_bytes(self) -> int:
+        """Get the total size (in bytes) of all the weights in the model."""
+        return self._total_bytes
+
+    def cur_vram_bytes(self) -> int:
+        """Get the size (in bytes) of the weights that are currently in VRAM."""
+        if self._is_in_vram:
+            return self._total_bytes
+        else:
+            return 0
+
+    def is_in_vram(self) -> bool:
+        """Return true if the model is currently in VRAM."""
+        return self._is_in_vram
+
+    def full_load_to_vram(self) -> int:
+        """Load all weights into VRAM (if supported by the model).
+
+        Returns:
+            The number of bytes loaded into VRAM.
+        """
+        if self._is_in_vram:
+            # Already in VRAM.
+            return 0
+
+        if not hasattr(self._model, "to"):
+            # Model doesn't support moving to a device.
+            return 0
+
+        self._model.to(self._compute_device)
+        self._is_in_vram = True
+        return self._total_bytes
+
+    def full_unload_from_vram(self) -> int:
+        """Unload all weights from VRAM.
+
+        Returns:
+            The number of bytes unloaded from VRAM.
+        """
+        if not self._is_in_vram:
+            # Already in RAM.
+            return 0
+
+        self._model.to("cpu")
+        self._is_in_vram = False
+        return self._total_bytes

invokeai/backend/model_manager/load/model_cache/cached_model/cached_model_with_partial_load.py

@@ -0,0 +1,139 @@
+import torch
+
+from invokeai.backend.model_manager.load.model_cache.torch_function_autocast_context import (
+    add_autocast_to_module_forward,
+)
+from invokeai.backend.util.calc_tensor_size import calc_tensor_size
+
+
+def set_nested_attr(obj: object, attr: str, value: object):
+    """A helper function that extends setattr() to support nested attributes.
+
+    Example:
+        set_nested_attr(model, "module.encoder.conv1.weight", new_conv1_weight)
+    """
+    attrs = attr.split(".")
+    for attr in attrs[:-1]:
+        obj = getattr(obj, attr)
+    setattr(obj, attrs[-1], value)
+
+
+class CachedModelWithPartialLoad:
+    """A wrapper around a PyTorch model to handle partial loads and unloads between the CPU and the compute device.
+
+    Note: "VRAM" is used throughout this class to refer to the memory on the compute device. It could be CUDA memory,
+    MPS memory, etc.
+    """
+
+    def __init__(self, model: torch.nn.Module, compute_device: torch.device):
+        self._model = model
+        self._compute_device = compute_device
+
+        # A CPU read-only copy of the model's state dict.
+        self._cpu_state_dict: dict[str, torch.Tensor] = model.state_dict()
+
+        # Monkey-patch the model to add autocasting to the model's forward method.
+        add_autocast_to_module_forward(model, compute_device)
+
+        # TODO(ryand): Manage a read-only CPU copy of the model state dict.
+        # TODO(ryand): Add memoization for total_bytes and cur_vram_bytes?
+
+        self._total_bytes = sum(calc_tensor_size(p) for p in self._model.parameters())
+        self._cur_vram_bytes: int | None = None
+
+    @property
+    def model(self) -> torch.nn.Module:
+        return self._model
+
+    def get_cpu_state_dict(self) -> dict[str, torch.Tensor] | None:
+        """Get a read-only copy of the model's state dict in RAM."""
+        # TODO(ryand): Document this better.
+        return self._cpu_state_dict
+
+    def total_bytes(self) -> int:
+        """Get the total size (in bytes) of all the weights in the model."""
+        return self._total_bytes
+
+    def cur_vram_bytes(self) -> int:
+        """Get the size (in bytes) of the weights that are currently in VRAM."""
+        if self._cur_vram_bytes is None:
+            self._cur_vram_bytes = sum(
+                calc_tensor_size(p) for p in self._model.parameters() if p.device.type == self._compute_device.type
+            )
+        return self._cur_vram_bytes
+
+    def full_load_to_vram(self) -> int:
+        """Load all weights into VRAM."""
+        return self.partial_load_to_vram(self.total_bytes())
+
+    def full_unload_from_vram(self) -> int:
+        """Unload all weights from VRAM."""
+        return self.partial_unload_from_vram(self.total_bytes())
+
+    @torch.no_grad()
+    def partial_load_to_vram(self, vram_bytes_to_load: int) -> int:
+        """Load more weights into VRAM without exceeding vram_bytes_to_load.
+
+        Returns:
+            The number of bytes loaded into VRAM.
+        """
+        vram_bytes_loaded = 0
+
+        # TODO(ryand): Iterate over buffers too?
+        for key, param in self._model.named_parameters():
+            # Skip parameters that are already on the compute device.
+            if param.device.type == self._compute_device.type:
+                continue
+
+            # Check the size of the parameter.
+            param_size = calc_tensor_size(param)
+            if vram_bytes_loaded + param_size > vram_bytes_to_load:
+                # TODO(ryand): Should we just break here? If we couldn't fit this parameter into VRAM, is it really
+                # worth continuing to search for a smaller parameter that would fit?
+                continue
+
+            # Copy the parameter to the compute device.
+            # We use the 'overwrite' strategy from torch.nn.Module._apply().
+            # TODO(ryand): For some edge cases (e.g. quantized models?), we may need to support other strategies (e.g.
+            # swap).
+            assert isinstance(param, torch.nn.Parameter)
+            assert param.is_leaf
+            out_param = torch.nn.Parameter(param.to(self._compute_device, copy=True), requires_grad=param.requires_grad)
+            set_nested_attr(self._model, key, out_param)
+            # We did not port the param.grad handling from torch.nn.Module._apply(), because we do not expect to be
+            # handling gradients. We assert that this assumption is true.
+            assert param.grad is None
+
+            vram_bytes_loaded += param_size
+
+        if self._cur_vram_bytes is not None:
+            self._cur_vram_bytes += vram_bytes_loaded
+
+        return vram_bytes_loaded
+
+    @torch.no_grad()
+    def partial_unload_from_vram(self, vram_bytes_to_free: int) -> int:
+        """Unload weights from VRAM until vram_bytes_to_free bytes are freed. Or the entire model is unloaded.
+
+        Returns:
+            The number of bytes unloaded from VRAM.
+        """
+        vram_bytes_freed = 0
+
+        # TODO(ryand): Iterate over buffers too?
+        for key, param in self._model.named_parameters():
+            if vram_bytes_freed >= vram_bytes_to_free:
+                break
+
+            if param.device.type != self._compute_device.type:
+                continue
+
+            # Create a new parameter, but inject the existing CPU tensor into it.
+            out_param = torch.nn.Parameter(self._cpu_state_dict[key], requires_grad=param.requires_grad)
+            set_nested_attr(self._model, key, out_param)
+            vram_bytes_freed += calc_tensor_size(param)
+
+        if self._cur_vram_bytes is not None:
+            self._cur_vram_bytes -= vram_bytes_freed
+
+        return vram_bytes_freed

invokeai/backend/model_manager/load/model_cache/model_cache.py

@@ -0,0 +1,534 @@
+import gc
+from logging import Logger
+from typing import Dict, List, Optional
+
+import torch
+
+from invokeai.backend.model_manager import AnyModel, SubModelType
+from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_only_full_load import (
+    CachedModelOnlyFullLoad,
+)
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_with_partial_load import (
+    CachedModelWithPartialLoad,
+)
+from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.logging import InvokeAILogger
+from invokeai.backend.util.prefix_logger_adapter import PrefixedLoggerAdapter
+
+# Size of a GB in bytes.
+GB = 2**30
+
+# Size of a MB in bytes.
+MB = 2**20
+
+
+# TODO(ryand): Where should this go? The ModelCache shouldn't be concerned with submodels.
+def get_model_cache_key(model_key: str, submodel_type: Optional[SubModelType] = None) -> str:
+    """Get the cache key for a model based on the optional submodel type."""
+    if submodel_type:
+        return f"{model_key}:{submodel_type.value}"
+    else:
+        return model_key
+
+
+class ModelCache:
+    """A cache for managing models in memory.
+
+    The cache is based on two levels of model storage:
+    - execution_device: The device where most models are executed (typically "cuda", "mps", or "cpu").
+    - storage_device: The device where models are offloaded when not in active use (typically "cpu").
+
+    The model cache is based on the following assumptions:
+    - storage_device_mem_size > execution_device_mem_size
+    - disk_to_storage_device_transfer_time >> storage_device_to_execution_device_transfer_time
+
+    A copy of all models in the cache is always kept on the storage_device. A subset of the models also have a copy on
+    the execution_device.
+
+    Models are moved between the storage_device and the execution_device as necessary. Cache size limits are enforced
+    on both the storage_device and the execution_device. The execution_device cache uses a smallest-first offload
+    policy. The storage_device cache uses a least-recently-used (LRU) offload policy.
+
+    Note: Neither of these offload policies has really been compared against alternatives. It's likely that different
+    policies would be better, although the optimal policies are likely heavily dependent on usage patterns and HW
+    configuration.
+
+    The cache returns context manager generators designed to load the model into the execution device (often GPU) within
+    the context, and unload outside the context.
+
+    Example usage:
+    ```
+    cache = ModelCache(max_cache_size=7.5, max_vram_cache_size=6.0)
+    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1:
+        do_something_on_gpu(SD1)
+    ```
+    """
+
+    def __init__(
+        self,
+        max_cache_size: float,
+        max_vram_cache_size: float,
+        execution_device: torch.device = torch.device("cuda"),
+        storage_device: torch.device = torch.device("cpu"),
+        lazy_offloading: bool = True,
+        log_memory_usage: bool = False,
+        logger: Optional[Logger] = None,
+    ):
+        """
+        Initialize the model RAM cache.
+
+        :param max_cache_size: Maximum size of the storage_device cache in GBs.
+        :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
+        :param execution_device: Torch device to load active model into [torch.device('cuda')]
+        :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
+        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
+        :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
+            operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
+            snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
+            behaviour.
+        :param logger: InvokeAILogger to use (otherwise creates one)
+        """
+        # allow lazy offloading only when vram cache enabled
+        # TODO(ryand): Think about what lazy_offloading should mean in the new model cache.
+        self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
+        self._max_cache_size: float = max_cache_size
+        self._max_vram_cache_size: float = max_vram_cache_size
+        self._execution_device: torch.device = execution_device
+        self._storage_device: torch.device = storage_device
+        self._logger = PrefixedLoggerAdapter(
+            logger or InvokeAILogger.get_logger(self.__class__.__name__), "MODEL CACHE"
+        )
+        self._log_memory_usage = log_memory_usage
+        self._stats: Optional[CacheStats] = None
+
+        self._cached_models: Dict[str, CacheRecord] = {}
+        self._cache_stack: List[str] = []
+
+    @property
+    def max_cache_size(self) -> float:
+        """Return the cap on cache size."""
+        return self._max_cache_size
+
+    @max_cache_size.setter
+    def max_cache_size(self, value: float) -> None:
+        """Set the cap on cache size."""
+        self._max_cache_size = value
+
+    @property
+    def max_vram_cache_size(self) -> float:
+        """Return the cap on vram cache size."""
+        return self._max_vram_cache_size
+
+    @max_vram_cache_size.setter
+    def max_vram_cache_size(self, value: float) -> None:
+        """Set the cap on vram cache size."""
+        self._max_vram_cache_size = value
+
+    @property
+    def stats(self) -> Optional[CacheStats]:
+        """Return collected CacheStats object."""
+        return self._stats
+
+    @stats.setter
+    def stats(self, stats: CacheStats) -> None:
+        """Set the CacheStats object for collecting cache statistics."""
+        self._stats = stats
+
+    def put(self, key: str, model: AnyModel) -> None:
+        """Add a model to the cache."""
+        if key in self._cached_models:
+            self._logger.debug(
+                f"Attempted to add model {key} ({model.__class__.__name__}), but it already exists in the cache. No action necessary."
+            )
+            return
+
+        size = calc_model_size_by_data(self._logger, model)
+        self.make_room(size)
+
+        # Wrap model.
+        if isinstance(model, torch.nn.Module):
+            wrapped_model = CachedModelWithPartialLoad(model, self._execution_device)
+        else:
+            wrapped_model = CachedModelOnlyFullLoad(model, self._execution_device, size)
+
+        # running_on_cpu = self._execution_device == torch.device("cpu")
+        # state_dict = model.state_dict() if isinstance(model, torch.nn.Module) and not running_on_cpu else None
+        cache_record = CacheRecord(key=key, cached_model=wrapped_model)
+        self._cached_models[key] = cache_record
+        self._cache_stack.append(key)
+        self._logger.debug(
+            f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size/MB:.2f}MB)"
+        )
+
+    def get(self, key: str, stats_name: Optional[str] = None) -> CacheRecord:
+        """Retrieve a model from the cache.
+
+        :param key: Model key
+        :param stats_name: A human-readable id for the model for the purposes of stats reporting.
+
+        Raises IndexError if the model is not in the cache.
+        """
+        if key in self._cached_models:
+            if self.stats:
+                self.stats.hits += 1
+        else:
+            if self.stats:
+                self.stats.misses += 1
+            self._logger.debug(f"Cache miss: {key}")
+            raise IndexError(f"The model with key {key} is not in the cache.")
+
+        cache_entry = self._cached_models[key]
+
+        # more stats
+        if self.stats:
+            stats_name = stats_name or key
+            self.stats.cache_size = int(self._max_cache_size * GB)
+            self.stats.high_watermark = max(self.stats.high_watermark, self._get_ram_in_use())
+            self.stats.in_cache = len(self._cached_models)
+            self.stats.loaded_model_sizes[stats_name] = max(
+                self.stats.loaded_model_sizes.get(stats_name, 0), cache_entry.cached_model.total_bytes()
+            )
+
+        # this moves the entry to the top (right end) of the stack
+        self._cache_stack = [k for k in self._cache_stack if k != key]
+        self._cache_stack.append(key)
+
+        self._logger.debug(f"Cache hit: {key} (Type: {cache_entry.cached_model.model.__class__.__name__})")
+
+        return cache_entry
+
+    def lock(self, key: str) -> None:
+        """Lock a model for use and move it into VRAM."""
+        cache_entry = self._cached_models[key]
+        cache_entry.lock()
+
+        self._logger.debug(f"Locking model {key} (Type: {cache_entry.cached_model.model.__class__.__name__})")
+
+        try:
+            self._load_locked_model(cache_entry)
+            self._logger.debug(
+                f"Finished locking model {key} (Type: {cache_entry.cached_model.model.__class__.__name__})"
+            )
+        except torch.cuda.OutOfMemoryError:
+            self._logger.warning("Insufficient GPU memory to load model. Aborting")
+            cache_entry.unlock()
+            raise
+        except Exception:
+            cache_entry.unlock()
+            raise
+
+        self._log_cache_state()
+
+    def unlock(self, key: str) -> None:
+        """Unlock a model."""
+        cache_entry = self._cached_models[key]
+        cache_entry.unlock()
+        self._logger.debug(f"Unlocked model {key} (Type: {cache_entry.cached_model.model.__class__.__name__})")
+
+    def _load_locked_model(self, cache_entry: CacheRecord) -> None:
+        """Helper function for self.lock(). Loads a locked model into VRAM."""
+        vram_available = self._get_vram_available()
+
+        # The amount of additional VRAM that will be used if we fully load the model into VRAM.
+        model_cur_vram_bytes = cache_entry.cached_model.cur_vram_bytes()
+        model_total_bytes = cache_entry.cached_model.total_bytes()
+        model_vram_needed = model_total_bytes - model_cur_vram_bytes
+
+        self._logger.debug(
+            f"Before unloading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
+        )
+
+        # Make room for the model in VRAM.
+        # 1. If the model can fit entirely in VRAM, then make enough room for it to be loaded fully.
+        # 2. If the model can't fit fully into VRAM, then unload all other models and load as much of the model as
+        #    possible.
+        vram_bytes_freed = self._offload_unlocked_models(model_vram_needed)
+        self._logger.debug(f"Unloaded models (if necessary): vram_bytes_freed={(vram_bytes_freed/MB):.2f}MB")
+
+        # Check the updated vram_available after offloading.
+        vram_available = self._get_vram_available()
+        self._logger.debug(
+            f"After unloading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
+        )
+
+        # Move as much of the model as possible into VRAM.
+        model_bytes_loaded = 0
+        if isinstance(cache_entry.cached_model, CachedModelWithPartialLoad):
+            model_bytes_loaded = cache_entry.cached_model.partial_load_to_vram(vram_available)
+        elif isinstance(cache_entry.cached_model, CachedModelOnlyFullLoad):  # type: ignore
+            # Partial load is not supported, so we have not choice but to try and fit it all into VRAM.
+            model_bytes_loaded = cache_entry.cached_model.full_load_to_vram()
+        else:
+            raise ValueError(f"Unsupported cached model type: {type(cache_entry.cached_model)}")
+
+        model_cur_vram_bytes = cache_entry.cached_model.cur_vram_bytes()
+        vram_available = self._get_vram_available()
+        self._logger.debug(f"Loaded model onto execution device: model_bytes_loaded={(model_bytes_loaded/MB):.2f}MB, ")
+        self._logger.debug(
+            f"After loading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
+        )
+
+    def _get_vram_available(self) -> int:
+        """Get the amount of VRAM available in the cache."""
+        return int(self._max_vram_cache_size * GB) - self._get_vram_in_use()
+
+    def _get_vram_in_use(self) -> int:
+        """Get the amount of VRAM currently in use."""
+        return sum(ce.cached_model.cur_vram_bytes() for ce in self._cached_models.values())
+
+    def _get_ram_available(self) -> int:
+        """Get the amount of RAM available in the cache."""
+        return int(self._max_cache_size * GB) - self._get_ram_in_use()
+
+    def _get_ram_in_use(self) -> int:
+        """Get the amount of RAM currently in use."""
+        return sum(ce.cached_model.total_bytes() for ce in self._cached_models.values())
+
+    def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
+        if self._log_memory_usage:
+            return MemorySnapshot.capture()
+        return None
+
+    def _get_vram_state_str(self, model_cur_vram_bytes: int, model_total_bytes: int, vram_available: int) -> str:
+        """Helper function for preparing a VRAM state log string."""
+        model_cur_vram_bytes_percent = model_cur_vram_bytes / model_total_bytes if model_total_bytes > 0 else 0
+        return (
+            f"model_total={model_total_bytes/MB:.0f} MB, "
+            + f"model_vram={model_cur_vram_bytes/MB:.0f} MB ({model_cur_vram_bytes_percent:.1%} %), "
+            + f"vram_total={int(self._max_vram_cache_size * GB)/MB:.0f} MB, "
+            + f"vram_available={(vram_available/MB):.0f} MB, "
+        )
+
+    def _offload_unlocked_models(self, vram_bytes_to_free: int) -> int:
+        """Offload models from the execution_device until vram_bytes_to_free bytes are freed, or all models are
+        offloaded. Of course, locked models are not offloaded.
+
+        Returns:
+            int: The number of bytes freed.
+        """
+        self._logger.debug(f"Offloading unlocked models with goal of freeing {vram_bytes_to_free/MB:.2f}MB of VRAM.")
+        vram_bytes_freed = 0
+        # TODO(ryand): Give more thought to the offloading policy used here.
+        cache_entries_increasing_size = sorted(self._cached_models.values(), key=lambda x: x.cached_model.total_bytes())
+        for cache_entry in cache_entries_increasing_size:
+            if vram_bytes_freed >= vram_bytes_to_free:
+                break
+            if cache_entry.is_locked:
+                continue
+
+            if isinstance(cache_entry.cached_model, CachedModelWithPartialLoad):
+                cache_entry_bytes_freed = cache_entry.cached_model.partial_unload_from_vram(
+                    vram_bytes_to_free - vram_bytes_freed
+                )
+            elif isinstance(cache_entry.cached_model, CachedModelOnlyFullLoad):  # type: ignore
+                cache_entry_bytes_freed = cache_entry.cached_model.full_unload_from_vram()
+            else:
+                raise ValueError(f"Unsupported cached model type: {type(cache_entry.cached_model)}")
+            if cache_entry_bytes_freed > 0:
+                self._logger.debug(
+                    f"Unloaded {cache_entry.key} from VRAM to free {(cache_entry_bytes_freed/MB):.0f} MB."
+                )
+            vram_bytes_freed += cache_entry_bytes_freed
+
+        TorchDevice.empty_cache()
+        return vram_bytes_freed
+
+    # def _move_model_to_device(self, cache_entry: CacheRecord, target_device: torch.device) -> None:
+    #     """Move model into the indicated device.
+
+    #     :param cache_entry: The CacheRecord for the model
+    #     :param target_device: The torch.device to move the model into
+
+    #     May raise a torch.cuda.OutOfMemoryError
+    #     """
+    #     self._logger.debug(f"Called to move {cache_entry.key} to {target_device}")
+    #     source_device = cache_entry.device
+
+    #     # Note: We compare device types only so that 'cuda' == 'cuda:0'.
+    #     # This would need to be revised to support multi-GPU.
+    #     if torch.device(source_device).type == torch.device(target_device).type:
+    #         return
+
+    #     # Some models don't have a `to` method, in which case they run in RAM/CPU.
+    #     if not hasattr(cache_entry.model, "to"):
+    #         return
+
+    #     # This roundabout method for moving the model around is done to avoid
+    #     # the cost of moving the model from RAM to VRAM and then back from VRAM to RAM.
+    #     # When moving to VRAM, we copy (not move) each element of the state dict from
+    #     # RAM to a new state dict in VRAM, and then inject it into the model.
+    #     # This operation is slightly faster than running `to()` on the whole model.
+    #     #
+    #     # When the model needs to be removed from VRAM we simply delete the copy
+    #     # of the state dict in VRAM, and reinject the state dict that is cached
+    #     # in RAM into the model. So this operation is very fast.
+    #     start_model_to_time = time.time()
+    #     snapshot_before = self._capture_memory_snapshot()
+
+    #     try:
+    #         if cache_entry.state_dict is not None:
+    #             assert hasattr(cache_entry.model, "load_state_dict")
+    #             if target_device == self._storage_device:
+    #                 cache_entry.model.load_state_dict(cache_entry.state_dict, assign=True)
+    #             else:
+    #                 new_dict: Dict[str, torch.Tensor] = {}
+    #                 for k, v in cache_entry.state_dict.items():
+    #                     new_dict[k] = v.to(target_device, copy=True)
+    #                 cache_entry.model.load_state_dict(new_dict, assign=True)
+    #         cache_entry.model.to(target_device)
+    #         cache_entry.device = target_device
+    #     except Exception as e:  # blow away cache entry
+    #         self._delete_cache_entry(cache_entry)
+    #         raise e
+
+    #     snapshot_after = self._capture_memory_snapshot()
+    #     end_model_to_time = time.time()
+    #     self._logger.debug(
+    #         f"Moved model '{cache_entry.key}' from {source_device} to"
+    #         f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
+    #         f"Estimated model size: {(cache_entry.size/GB):.3f} GB."
+    #         f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
+    #     )
+
+    #     if (
+    #         snapshot_before is not None
+    #         and snapshot_after is not None
+    #         and snapshot_before.vram is not None
+    #         and snapshot_after.vram is not None
+    #     ):
+    #         vram_change = abs(snapshot_before.vram - snapshot_after.vram)
+
+    #         # If the estimated model size does not match the change in VRAM, log a warning.
+    #         if not math.isclose(
+    #             vram_change,
+    #             cache_entry.size,
+    #             rel_tol=0.1,
+    #             abs_tol=10 * MB,
+    #         ):
+    #             self._logger.debug(
+    #                 f"Moving model '{cache_entry.key}' from {source_device} to"
+    #                 f" {target_device} caused an unexpected change in VRAM usage. The model's"
+    #                 " estimated size may be incorrect. Estimated model size:"
+    #                 f" {(cache_entry.size/GB):.3f} GB.\n"
+    #                 f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
+    #             )
+
+    def _log_cache_state(self, title: str = "Model cache state:", include_entry_details: bool = True):
+        ram_size_bytes = self._max_cache_size * GB
+        ram_in_use_bytes = self._get_ram_in_use()
+        ram_in_use_bytes_percent = ram_in_use_bytes / ram_size_bytes if ram_size_bytes > 0 else 0
+        ram_available_bytes = self._get_ram_available()
+        ram_available_bytes_percent = ram_available_bytes / ram_size_bytes if ram_size_bytes > 0 else 0
+
+        vram_size_bytes = self._max_vram_cache_size * GB
+        vram_in_use_bytes = self._get_vram_in_use()
+        vram_in_use_bytes_percent = vram_in_use_bytes / vram_size_bytes if vram_size_bytes > 0 else 0
+        vram_available_bytes = self._get_vram_available()
+        vram_available_bytes_percent = vram_available_bytes / vram_size_bytes if vram_size_bytes > 0 else 0
+
+        log = f"{title}\n"
+
+        log_format = "  {:<30} Limit: {:>7.1f} MB, Used: {:>7.1f} MB ({:>5.1%}), Available: {:>7.1f} MB ({:>5.1%})\n"
+        log += log_format.format(
+            f"Storage Device ({self._storage_device.type})",
+            ram_size_bytes / MB,
+            ram_in_use_bytes / MB,
+            ram_in_use_bytes_percent,
+            ram_available_bytes / MB,
+            ram_available_bytes_percent,
+        )
+        log += log_format.format(
+            f"Compute Device ({self._execution_device.type})",
+            vram_size_bytes / MB,
+            vram_in_use_bytes / MB,
+            vram_in_use_bytes_percent,
+            vram_available_bytes / MB,
+            vram_available_bytes_percent,
+        )
+
+        if torch.cuda.is_available():
+            log += "  {:<30} {} MB\n".format("CUDA Memory Allocated:", torch.cuda.memory_allocated() / MB)
+        log += "  {:<30} {}\n".format("Total models:", len(self._cached_models))
+
+        if include_entry_details and len(self._cached_models) > 0:
+            log += "  Models:\n"
+            log_format = (
+                "    {:<80} total={:>7.1f} MB, vram={:>7.1f} MB ({:>5.1%}), ram={:>7.1f} MB ({:>5.1%}), locked={}\n"
+            )
+            for cache_record in self._cached_models.values():
+                total_bytes = cache_record.cached_model.total_bytes()
+                cur_vram_bytes = cache_record.cached_model.cur_vram_bytes()
+                cur_vram_bytes_percent = cur_vram_bytes / total_bytes if total_bytes > 0 else 0
+                cur_ram_bytes = total_bytes - cur_vram_bytes
+                cur_ram_bytes_percent = cur_ram_bytes / total_bytes if total_bytes > 0 else 0
+
+                log += log_format.format(
+                    f"{cache_record.key} ({cache_record.cached_model.model.__class__.__name__}):",
+                    total_bytes / MB,
+                    cur_vram_bytes / MB,
+                    cur_vram_bytes_percent,
+                    cur_ram_bytes / MB,
+                    cur_ram_bytes_percent,
+                    cache_record.is_locked,
+                )
+
+        self._logger.debug(log)
+
+    def make_room(self, bytes_needed: int) -> None:
+        """Make enough room in the cache to accommodate a new model of indicated size.
+
+        Note: This function deletes all of the cache's internal references to a model in order to free it. If there are
+        external references to the model, there's nothing that the cache can do about it, and those models will not be
+        garbage-collected.
+        """
+        self._logger.debug(f"Making room for {bytes_needed/MB:.2f}MB of RAM.")
+        self._log_cache_state(title="Before dropping models:")
+
+        ram_bytes_available = self._get_ram_available()
+        ram_bytes_to_free = max(0, bytes_needed - ram_bytes_available)
+
+        ram_bytes_freed = 0
+        pos = 0
+        models_cleared = 0
+        while ram_bytes_freed < ram_bytes_to_free and pos < len(self._cache_stack):
+            model_key = self._cache_stack[pos]
+            cache_entry = self._cached_models[model_key]
+
+            if not cache_entry.is_locked:
+                ram_bytes_freed += cache_entry.cached_model.total_bytes()
+                self._logger.debug(
+                    f"Dropping {model_key} from RAM cache to free {(cache_entry.cached_model.total_bytes()/MB):.2f}MB."
+                )
+                self._delete_cache_entry(cache_entry)
+                del cache_entry
+                models_cleared += 1
+            else:
+                pos += 1
+
+        if models_cleared > 0:
+            # There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but
+            # there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost
+            # is high even if no garbage gets collected.)
+            #
+            # Calling gc.collect(...) when a model is cleared seems like a good middle-ground:
+            # - If models had to be cleared, it's a signal that we are close to our memory limit.
+            # - If models were cleared, there's a good chance that there's a significant amount of garbage to be
+            #   collected.
+            #
+            # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
+            # immediately when their reference count hits 0.
+            if self.stats:
+                self.stats.cleared = models_cleared
+            gc.collect()
+
+        TorchDevice.empty_cache()
+        self._logger.debug(f"Dropped {models_cleared} models to free {ram_bytes_freed/MB:.2f}MB of RAM.")
+        self._log_cache_state(title="After dropping models:")
+
+    def _delete_cache_entry(self, cache_entry: CacheRecord) -> None:
+        self._cache_stack.remove(cache_entry.key)
+        del self._cached_models[cache_entry.key]

invokeai/backend/model_manager/load/model_cache/model_cache_base.py

@@ -1,221 +0,0 @@
-# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
-# TODO: Add Stalker's proper name to copyright
-"""
-Manage a RAM cache of diffusion/transformer models for fast switching.
-They are moved between GPU VRAM and CPU RAM as necessary. If the cache
-grows larger than a preset maximum, then the least recently used
-model will be cleared and (re)loaded from disk when next needed.
-"""
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass, field
-from logging import Logger
-from typing import Dict, Generic, Optional, TypeVar
-
-import torch
-
-from invokeai.backend.model_manager.config import AnyModel, SubModelType
-
-
-class ModelLockerBase(ABC):
-    """Base class for the model locker used by the loader."""
-
-    @abstractmethod
-    def lock(self) -> AnyModel:
-        """Lock the contained model and move it into VRAM."""
-        pass
-
-    @abstractmethod
-    def unlock(self) -> None:
-        """Unlock the contained model, and remove it from VRAM."""
-        pass
-
-    @abstractmethod
-    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
-        """Return the state dict (if any) for the cached model."""
-        pass
-
-    @property
-    @abstractmethod
-    def model(self) -> AnyModel:
-        """Return the model."""
-        pass
-
-
-T = TypeVar("T")
-
-
-@dataclass
-class CacheRecord(Generic[T]):
-    """
-    Elements of the cache:
-
-    key: Unique key for each model, same as used in the models database.
-    model: Model in memory.
-    state_dict: A read-only copy of the model's state dict in RAM. It will be
-                used as a template for creating a copy in the VRAM.
-    size: Size of the model
-    loaded: True if the model's state dict is currently in VRAM
-
-    Before a model is executed, the state_dict template is copied into VRAM,
-    and then injected into the model. When the model is finished, the VRAM
-    copy of the state dict is deleted, and the RAM version is reinjected
-    into the model.
-
-    The state_dict should be treated as a read-only attribute. Do not attempt
-    to patch or otherwise modify it. Instead, patch the copy of the state_dict
-    after it is loaded into the execution device (e.g. CUDA) using the `LoadedModel`
-    context manager call `model_on_device()`.
-    """
-
-    key: str
-    model: T
-    device: torch.device
-    state_dict: Optional[Dict[str, torch.Tensor]]
-    size: int
-    loaded: bool = False
-    _locks: int = 0
-
-    def lock(self) -> None:
-        """Lock this record."""
-        self._locks += 1
-
-    def unlock(self) -> None:
-        """Unlock this record."""
-        self._locks -= 1
-        assert self._locks >= 0
-
-    @property
-    def locked(self) -> bool:
-        """Return true if record is locked."""
-        return self._locks > 0
-
-
-@dataclass
-class CacheStats(object):
-    """Collect statistics on cache performance."""
-
-    hits: int = 0  # cache hits
-    misses: int = 0  # cache misses
-    high_watermark: int = 0  # amount of cache used
-    in_cache: int = 0  # number of models in cache
-    cleared: int = 0  # number of models cleared to make space
-    cache_size: int = 0  # total size of cache
-    loaded_model_sizes: Dict[str, int] = field(default_factory=dict)
-
-
-class ModelCacheBase(ABC, Generic[T]):
-    """Virtual base class for RAM model cache."""
-
-    @property
-    @abstractmethod
-    def storage_device(self) -> torch.device:
-        """Return the storage device (e.g. "CPU" for RAM)."""
-        pass
-
-    @property
-    @abstractmethod
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
-        pass
-
-    @property
-    @abstractmethod
-    def lazy_offloading(self) -> bool:
-        """Return true if the cache is configured to lazily offload models in VRAM."""
-        pass
-
-    @property
-    @abstractmethod
-    def max_cache_size(self) -> float:
-        """Return the maximum size the RAM cache can grow to."""
-        pass
-
-    @max_cache_size.setter
-    @abstractmethod
-    def max_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-
-    @property
-    @abstractmethod
-    def max_vram_cache_size(self) -> float:
-        """Return the maximum size the VRAM cache can grow to."""
-        pass
-
-    @max_vram_cache_size.setter
-    @abstractmethod
-    def max_vram_cache_size(self, value: float) -> float:
-        """Set the maximum size the VRAM cache can grow to."""
-        pass
-
-    @abstractmethod
-    def offload_unlocked_models(self, size_required: int) -> None:
-        """Offload from VRAM any models not actively in use."""
-        pass
-
-    @abstractmethod
-    def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
-        """Move model into the indicated device."""
-        pass
-
-    @property
-    @abstractmethod
-    def stats(self) -> Optional[CacheStats]:
-        """Return collected CacheStats object."""
-        pass
-
-    @stats.setter
-    @abstractmethod
-    def stats(self, stats: CacheStats) -> None:
-        """Set the CacheStats object for collectin cache statistics."""
-        pass
-
-    @property
-    @abstractmethod
-    def logger(self) -> Logger:
-        """Return the logger used by the cache."""
-        pass
-
-    @abstractmethod
-    def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size."""
-        pass
-
-    @abstractmethod
-    def put(
-        self,
-        key: str,
-        model: T,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> None:
-        """Store model under key and optional submodel_type."""
-        pass
-
-    @abstractmethod
-    def get(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        stats_name: Optional[str] = None,
-    ) -> ModelLockerBase:
-        """
-        Retrieve model using key and optional submodel_type.
-
-        :param key: Opaque model key
-        :param submodel_type: Type of the submodel to fetch
-        :param stats_name: A human-readable id for the model for the purposes of
-        stats reporting.
-
-        This may raise an IndexError if the model is not in the cache.
-        """
-        pass
-
-    @abstractmethod
-    def cache_size(self) -> int:
-        """Get the total size of the models currently cached."""
-        pass
-
-    @abstractmethod
-    def print_cuda_stats(self) -> None:
-        """Log debugging information on CUDA usage."""
-        pass

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -1,426 +0,0 @@
-# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
-# TODO: Add Stalker's proper name to copyright
-""" """
-
-import gc
-import math
-import time
-from contextlib import suppress
-from logging import Logger
-from typing import Dict, List, Optional
-
-import torch
-
-from invokeai.backend.model_manager import AnyModel, SubModelType
-from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import (
-    CacheRecord,
-    CacheStats,
-    ModelCacheBase,
-    ModelLockerBase,
-)
-from invokeai.backend.model_manager.load.model_cache.model_locker import ModelLocker
-from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
-from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.logging import InvokeAILogger
-
-# Size of a GB in bytes.
-GB = 2**30
-
-# Size of a MB in bytes.
-MB = 2**20
-
-
-class ModelCache(ModelCacheBase[AnyModel]):
-    """A cache for managing models in memory.
-
-    The cache is based on two levels of model storage:
-    - execution_device: The device where most models are executed (typically "cuda", "mps", or "cpu").
-    - storage_device: The device where models are offloaded when not in active use (typically "cpu").
-
-    The model cache is based on the following assumptions:
-    - storage_device_mem_size > execution_device_mem_size
-    - disk_to_storage_device_transfer_time >> storage_device_to_execution_device_transfer_time
-
-    A copy of all models in the cache is always kept on the storage_device. A subset of the models also have a copy on
-    the execution_device.
-
-    Models are moved between the storage_device and the execution_device as necessary. Cache size limits are enforced
-    on both the storage_device and the execution_device. The execution_device cache uses a smallest-first offload
-    policy. The storage_device cache uses a least-recently-used (LRU) offload policy.
-
-    Note: Neither of these offload policies has really been compared against alternatives. It's likely that different
-    policies would be better, although the optimal policies are likely heavily dependent on usage patterns and HW
-    configuration.
-
-    The cache returns context manager generators designed to load the model into the execution device (often GPU) within
-    the context, and unload outside the context.
-
-    Example usage:
-    ```
-    cache = ModelCache(max_cache_size=7.5, max_vram_cache_size=6.0)
-    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1:
-        do_something_on_gpu(SD1)
-    ```
-    """
-
-    def __init__(
-        self,
-        max_cache_size: float,
-        max_vram_cache_size: float,
-        execution_device: torch.device = torch.device("cuda"),
-        storage_device: torch.device = torch.device("cpu"),
-        precision: torch.dtype = torch.float16,
-        lazy_offloading: bool = True,
-        log_memory_usage: bool = False,
-        logger: Optional[Logger] = None,
-    ):
-        """
-        Initialize the model RAM cache.
-
-        :param max_cache_size: Maximum size of the storage_device cache in GBs.
-        :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
-        :param execution_device: Torch device to load active model into [torch.device('cuda')]
-        :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
-        :param precision: Precision for loaded models [torch.float16]
-        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
-        :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
-            operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
-            snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
-            behaviour.
-        :param logger: InvokeAILogger to use (otherwise creates one)
-        """
-        # allow lazy offloading only when vram cache enabled
-        self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
-        self._max_cache_size: float = max_cache_size
-        self._max_vram_cache_size: float = max_vram_cache_size
-        self._execution_device: torch.device = execution_device
-        self._storage_device: torch.device = storage_device
-        self._logger = logger or InvokeAILogger.get_logger(self.__class__.__name__)
-        self._log_memory_usage = log_memory_usage
-        self._stats: Optional[CacheStats] = None
-
-        self._cached_models: Dict[str, CacheRecord[AnyModel]] = {}
-        self._cache_stack: List[str] = []
-
-    @property
-    def logger(self) -> Logger:
-        """Return the logger used by the cache."""
-        return self._logger
-
-    @property
-    def lazy_offloading(self) -> bool:
-        """Return true if the cache is configured to lazily offload models in VRAM."""
-        return self._lazy_offloading
-
-    @property
-    def storage_device(self) -> torch.device:
-        """Return the storage device (e.g. "CPU" for RAM)."""
-        return self._storage_device
-
-    @property
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
-        return self._execution_device
-
-    @property
-    def max_cache_size(self) -> float:
-        """Return the cap on cache size."""
-        return self._max_cache_size
-
-    @max_cache_size.setter
-    def max_cache_size(self, value: float) -> None:
-        """Set the cap on cache size."""
-        self._max_cache_size = value
-
-    @property
-    def max_vram_cache_size(self) -> float:
-        """Return the cap on vram cache size."""
-        return self._max_vram_cache_size
-
-    @max_vram_cache_size.setter
-    def max_vram_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-        self._max_vram_cache_size = value
-
-    @property
-    def stats(self) -> Optional[CacheStats]:
-        """Return collected CacheStats object."""
-        return self._stats
-
-    @stats.setter
-    def stats(self, stats: CacheStats) -> None:
-        """Set the CacheStats object for collectin cache statistics."""
-        self._stats = stats
-
-    def cache_size(self) -> int:
-        """Get the total size of the models currently cached."""
-        total = 0
-        for cache_record in self._cached_models.values():
-            total += cache_record.size
-        return total
-
-    def put(
-        self,
-        key: str,
-        model: AnyModel,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> None:
-        """Store model under key and optional submodel_type."""
-        key = self._make_cache_key(key, submodel_type)
-        if key in self._cached_models:
-            return
-        size = calc_model_size_by_data(self.logger, model)
-        self.make_room(size)
-
-        running_on_cpu = self.execution_device == torch.device("cpu")
-        state_dict = model.state_dict() if isinstance(model, torch.nn.Module) and not running_on_cpu else None
-        cache_record = CacheRecord(key=key, model=model, device=self.storage_device, state_dict=state_dict, size=size)
-        self._cached_models[key] = cache_record
-        self._cache_stack.append(key)
-
-    def get(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        stats_name: Optional[str] = None,
-    ) -> ModelLockerBase:
-        """
-        Retrieve model using key and optional submodel_type.
-
-        :param key: Opaque model key
-        :param submodel_type: Type of the submodel to fetch
-        :param stats_name: A human-readable id for the model for the purposes of
-        stats reporting.
-
-        This may raise an IndexError if the model is not in the cache.
-        """
-        key = self._make_cache_key(key, submodel_type)
-        if key in self._cached_models:
-            if self.stats:
-                self.stats.hits += 1
-        else:
-            if self.stats:
-                self.stats.misses += 1
-            raise IndexError(f"The model with key {key} is not in the cache.")
-
-        cache_entry = self._cached_models[key]
-
-        # more stats
-        if self.stats:
-            stats_name = stats_name or key
-            self.stats.cache_size = int(self._max_cache_size * GB)
-            self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
-            self.stats.in_cache = len(self._cached_models)
-            self.stats.loaded_model_sizes[stats_name] = max(
-                self.stats.loaded_model_sizes.get(stats_name, 0), cache_entry.size
-            )
-
-        # this moves the entry to the top (right end) of the stack
-        with suppress(Exception):
-            self._cache_stack.remove(key)
-        self._cache_stack.append(key)
-        return ModelLocker(
-            cache=self,
-            cache_entry=cache_entry,
-        )
-
-    def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
-        if self._log_memory_usage:
-            return MemorySnapshot.capture()
-        return None
-
-    def _make_cache_key(self, model_key: str, submodel_type: Optional[SubModelType] = None) -> str:
-        if submodel_type:
-            return f"{model_key}:{submodel_type.value}"
-        else:
-            return model_key
-
-    def offload_unlocked_models(self, size_required: int) -> None:
-        """Offload models from the execution_device to make room for size_required.
-
-        :param size_required: The amount of space to clear in the execution_device cache, in bytes.
-        """
-        reserved = self._max_vram_cache_size * GB
-        vram_in_use = torch.cuda.memory_allocated() + size_required
-        self.logger.debug(f"{(vram_in_use/GB):.2f}GB VRAM needed for models; max allowed={(reserved/GB):.2f}GB")
-        for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
-            if vram_in_use <= reserved:
-                break
-            if not cache_entry.loaded:
-                continue
-            if not cache_entry.locked:
-                self.move_model_to_device(cache_entry, self.storage_device)
-                cache_entry.loaded = False
-                vram_in_use = torch.cuda.memory_allocated() + size_required
-                self.logger.debug(
-                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GB):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GB):.2f}GB"
-                )
-
-        TorchDevice.empty_cache()
-
-    def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
-        """Move model into the indicated device.
-
-        :param cache_entry: The CacheRecord for the model
-        :param target_device: The torch.device to move the model into
-
-        May raise a torch.cuda.OutOfMemoryError
-        """
-        self.logger.debug(f"Called to move {cache_entry.key} to {target_device}")
-        source_device = cache_entry.device
-
-        # Note: We compare device types only so that 'cuda' == 'cuda:0'.
-        # This would need to be revised to support multi-GPU.
-        if torch.device(source_device).type == torch.device(target_device).type:
-            return
-
-        # Some models don't have a `to` method, in which case they run in RAM/CPU.
-        if not hasattr(cache_entry.model, "to"):
-            return
-
-        # This roundabout method for moving the model around is done to avoid
-        # the cost of moving the model from RAM to VRAM and then back from VRAM to RAM.
-        # When moving to VRAM, we copy (not move) each element of the state dict from
-        # RAM to a new state dict in VRAM, and then inject it into the model.
-        # This operation is slightly faster than running `to()` on the whole model.
-        #
-        # When the model needs to be removed from VRAM we simply delete the copy
-        # of the state dict in VRAM, and reinject the state dict that is cached
-        # in RAM into the model. So this operation is very fast.
-        start_model_to_time = time.time()
-        snapshot_before = self._capture_memory_snapshot()
-
-        try:
-            if cache_entry.state_dict is not None:
-                assert hasattr(cache_entry.model, "load_state_dict")
-                if target_device == self.storage_device:
-                    cache_entry.model.load_state_dict(cache_entry.state_dict, assign=True)
-                else:
-                    new_dict: Dict[str, torch.Tensor] = {}
-                    for k, v in cache_entry.state_dict.items():
-                        new_dict[k] = v.to(target_device, copy=True)
-                    cache_entry.model.load_state_dict(new_dict, assign=True)
-            cache_entry.model.to(target_device)
-            cache_entry.device = target_device
-        except Exception as e:  # blow away cache entry
-            self._delete_cache_entry(cache_entry)
-            raise e
-
-        snapshot_after = self._capture_memory_snapshot()
-        end_model_to_time = time.time()
-        self.logger.debug(
-            f"Moved model '{cache_entry.key}' from {source_device} to"
-            f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
-            f"Estimated model size: {(cache_entry.size/GB):.3f} GB."
-            f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
-        )
-
-        if (
-            snapshot_before is not None
-            and snapshot_after is not None
-            and snapshot_before.vram is not None
-            and snapshot_after.vram is not None
-        ):
-            vram_change = abs(snapshot_before.vram - snapshot_after.vram)
-
-            # If the estimated model size does not match the change in VRAM, log a warning.
-            if not math.isclose(
-                vram_change,
-                cache_entry.size,
-                rel_tol=0.1,
-                abs_tol=10 * MB,
-            ):
-                self.logger.debug(
-                    f"Moving model '{cache_entry.key}' from {source_device} to"
-                    f" {target_device} caused an unexpected change in VRAM usage. The model's"
-                    " estimated size may be incorrect. Estimated model size:"
-                    f" {(cache_entry.size/GB):.3f} GB.\n"
-                    f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
-                )
-
-    def print_cuda_stats(self) -> None:
-        """Log CUDA diagnostics."""
-        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GB)
-        ram = "%4.2fG" % (self.cache_size() / GB)
-
-        in_ram_models = 0
-        in_vram_models = 0
-        locked_in_vram_models = 0
-        for cache_record in self._cached_models.values():
-            if hasattr(cache_record.model, "device"):
-                if cache_record.model.device == self.storage_device:
-                    in_ram_models += 1
-                else:
-                    in_vram_models += 1
-                if cache_record.locked:
-                    locked_in_vram_models += 1
-
-                self.logger.debug(
-                    f"Current VRAM/RAM usage: {vram}/{ram}; models_in_ram/models_in_vram(locked) ="
-                    f" {in_ram_models}/{in_vram_models}({locked_in_vram_models})"
-                )
-
-    def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size.
-
-        Note: This function deletes all of the cache's internal references to a model in order to free it. If there are
-        external references to the model, there's nothing that the cache can do about it, and those models will not be
-        garbage-collected.
-        """
-        bytes_needed = size
-        maximum_size = self.max_cache_size * GB  # stored in GB, convert to bytes
-        current_size = self.cache_size()
-
-        if current_size + bytes_needed > maximum_size:
-            self.logger.debug(
-                f"Max cache size exceeded: {(current_size/GB):.2f}/{self.max_cache_size:.2f} GB, need an additional"
-                f" {(bytes_needed/GB):.2f} GB"
-            )
-
-        self.logger.debug(f"Before making_room: cached_models={len(self._cached_models)}")
-
-        pos = 0
-        models_cleared = 0
-        while current_size + bytes_needed > maximum_size and pos < len(self._cache_stack):
-            model_key = self._cache_stack[pos]
-            cache_entry = self._cached_models[model_key]
-            device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
-            self.logger.debug(
-                f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}"
-            )
-
-            if not cache_entry.locked:
-                self.logger.debug(
-                    f"Removing {model_key} from RAM cache to free at least {(size/GB):.2f} GB (-{(cache_entry.size/GB):.2f} GB)"
-                )
-                current_size -= cache_entry.size
-                models_cleared += 1
-                self._delete_cache_entry(cache_entry)
-                del cache_entry
-
-            else:
-                pos += 1
-
-        if models_cleared > 0:
-            # There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but
-            # there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost
-            # is high even if no garbage gets collected.)
-            #
-            # Calling gc.collect(...) when a model is cleared seems like a good middle-ground:
-            # - If models had to be cleared, it's a signal that we are close to our memory limit.
-            # - If models were cleared, there's a good chance that there's a significant amount of garbage to be
-            #   collected.
-            #
-            # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
-            # immediately when their reference count hits 0.
-            if self.stats:
-                self.stats.cleared = models_cleared
-            gc.collect()
-
-        TorchDevice.empty_cache()
-        self.logger.debug(f"After making room: cached_models={len(self._cached_models)}")
-
-    def _delete_cache_entry(self, cache_entry: CacheRecord[AnyModel]) -> None:
-        self._cache_stack.remove(cache_entry.key)
-        del self._cached_models[cache_entry.key]

invokeai/backend/model_manager/load/model_cache/model_locker.py

@@ -1,64 +0,0 @@
-"""
-Base class and implementation of a class that moves models in and out of VRAM.
-"""
-
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.model_manager import AnyModel
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import (
-    CacheRecord,
-    ModelCacheBase,
-    ModelLockerBase,
-)
-
-
-class ModelLocker(ModelLockerBase):
-    """Internal class that mediates movement in and out of GPU."""
-
-    def __init__(self, cache: ModelCacheBase[AnyModel], cache_entry: CacheRecord[AnyModel]):
-        """
-        Initialize the model locker.
-
-        :param cache: The ModelCache object
-        :param cache_entry: The entry in the model cache
-        """
-        self._cache = cache
-        self._cache_entry = cache_entry
-
-    @property
-    def model(self) -> AnyModel:
-        """Return the model without moving it around."""
-        return self._cache_entry.model
-
-    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
-        """Return the state dict (if any) for the cached model."""
-        return self._cache_entry.state_dict
-
-    def lock(self) -> AnyModel:
-        """Move the model into the execution device (GPU) and lock it."""
-        self._cache_entry.lock()
-        try:
-            if self._cache.lazy_offloading:
-                self._cache.offload_unlocked_models(self._cache_entry.size)
-            self._cache.move_model_to_device(self._cache_entry, self._cache.execution_device)
-            self._cache_entry.loaded = True
-            self._cache.logger.debug(f"Locking {self._cache_entry.key} in {self._cache.execution_device}")
-            self._cache.print_cuda_stats()
-        except torch.cuda.OutOfMemoryError:
-            self._cache.logger.warning("Insufficient GPU memory to load model. Aborting")
-            self._cache_entry.unlock()
-            raise
-        except Exception:
-            self._cache_entry.unlock()
-            raise
-
-        return self.model
-
-    def unlock(self) -> None:
-        """Call upon exit from context."""
-        self._cache_entry.unlock()
-        if not self._cache.lazy_offloading:
-            self._cache.offload_unlocked_models(0)
-            self._cache.print_cuda_stats()

invokeai/backend/model_manager/load/model_cache/torch_function_autocast_context.py

@@ -0,0 +1,33 @@
+from typing import Any, Callable
+
+import torch
+from torch.overrides import TorchFunctionMode
+
+
+def add_autocast_to_module_forward(m: torch.nn.Module, to_device: torch.device):
+    """Monkey-patch m.forward(...) with a new forward(...) method that activates device autocasting for its duration."""
+    old_forward = m.forward
+
+    def new_forward(*args: Any, **kwargs: Any):
+        with TorchFunctionAutocastDeviceContext(to_device):
+            return old_forward(*args, **kwargs)
+
+    m.forward = new_forward
+
+
+def _cast_to_device_and_run(
+    func: Callable[..., Any], args: tuple[Any, ...], kwargs: dict[str, Any], to_device: torch.device
+):
+    args_on_device = [a.to(to_device) if isinstance(a, torch.Tensor) else a for a in args]
+    kwargs_on_device = {k: v.to(to_device) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
+    return func(*args_on_device, **kwargs_on_device)
+
+
+class TorchFunctionAutocastDeviceContext(TorchFunctionMode):
+    def __init__(self, to_device: torch.device):
+        self._to_device = to_device
+
+    def __torch_function__(
+        self, func: Callable[..., Any], types, args: tuple[Any, ...] = (), kwargs: dict[str, Any] | None = None
+    ):
+        return _cast_to_device_and_run(func, args, kwargs or {}, self._to_device)

invokeai/backend/model_manager/load/model_loaders/lora.py

@@ -26,7 +26,7 @@ from invokeai.backend.model_manager import (
     SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 
 
@@ -40,7 +40,7 @@ class LoRALoader(ModelLoader):
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         super().__init__(app_config, logger, ram_cache)

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -25,6 +25,7 @@ from invokeai.backend.model_manager.config import (
     DiffusersConfigBase,
     MainCheckpointConfig,
 )
+from invokeai.backend.model_manager.load.model_cache.model_cache import get_model_cache_key
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.util.silence_warnings import SilenceWarnings
@@ -132,5 +133,5 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
             if subtype == submodel_type:
                 continue
             if submodel := getattr(pipeline, subtype.value, None):
-                self._ram_cache.put(config.key, submodel_type=subtype, model=submodel)
+                self._ram_cache.put(get_model_cache_key(config.key, subtype), model=submodel)
         return getattr(pipeline, submodel_type.value)

invokeai/backend/util/prefix_logger_adapter.py

@@ -0,0 +1,12 @@
+import logging
+from typing import Any, MutableMapping
+
+
+# Issue with type hints related to LoggerAdapter: https://github.com/python/typeshed/issues/7855
+class PrefixedLoggerAdapter(logging.LoggerAdapter):  # type: ignore
+    def __init__(self, logger: logging.Logger, prefix: str):
+        super().__init__(logger, {})
+        self.prefix = prefix
+
+    def process(self, msg: str, kwargs: MutableMapping[str, Any]) -> tuple[str, MutableMapping[str, Any]]:
+        return f"[{self.prefix}] {msg}", kwargs

tests/backend/model_manager/load/model_cache/cached_model/test_cached_model_only_full_load.py

@@ -0,0 +1,50 @@
+import pytest
+import torch
+
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_only_full_load import (
+    CachedModelOnlyFullLoad,
+)
+from tests.backend.model_manager.load.model_cache.dummy_module import DummyModule
+
+parameterize_mps_and_cuda = pytest.mark.parametrize(
+    ("device"),
+    [
+        pytest.param(
+            "mps", marks=pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS is not available.")
+        ),
+        pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available.")),
+    ],
+)
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_total_bytes(device: str):
+    model = DummyModule()
+    cached_model = CachedModelOnlyFullLoad(model=model, compute_device=torch.device(device), total_bytes=100)
+    assert cached_model.total_bytes() == 100
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_is_in_vram(device: str):
+    model = DummyModule()
+    cached_model = CachedModelOnlyFullLoad(model=model, compute_device=torch.device(device), total_bytes=100)
+    assert not cached_model.is_in_vram()
+
+    cached_model.full_load_to_vram()
+    assert cached_model.is_in_vram()
+
+    cached_model.full_unload_from_vram()
+    assert not cached_model.is_in_vram()
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_full_load_and_unload(device: str):
+    model = DummyModule()
+    cached_model = CachedModelOnlyFullLoad(model=model, compute_device=torch.device(device), total_bytes=100)
+    assert cached_model.full_load_to_vram() == 100
+    assert cached_model.is_in_vram()
+    assert all(p.device.type == device for p in cached_model.model.parameters())
+
+    assert cached_model.full_unload_from_vram() == 100
+    assert not cached_model.is_in_vram()
+    assert all(p.device.type == "cpu" for p in cached_model.model.parameters())

tests/backend/model_manager/load/model_cache/cached_model/test_cached_model_with_partial_load.py

@@ -0,0 +1,174 @@
+import pytest
+import torch
+
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_with_partial_load import (
+    CachedModelWithPartialLoad,
+)
+from invokeai.backend.util.calc_tensor_size import calc_tensor_size
+from tests.backend.model_manager.load.model_cache.dummy_module import DummyModule
+
+parameterize_mps_and_cuda = pytest.mark.parametrize(
+    ("device"),
+    [
+        pytest.param(
+            "mps", marks=pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS is not available.")
+        ),
+        pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available.")),
+    ],
+)
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_total_bytes(device: str):
+    if device == "cuda" and not torch.cuda.is_available():
+        pytest.skip("CUDA is not available.")
+    if device == "mps" and not torch.backends.mps.is_available():
+        pytest.skip("MPS is not available.")
+
+    model = DummyModule()
+    cached_model = CachedModelWithPartialLoad(model=model, compute_device=torch.device(device))
+    linear_numel = 10 * 10 + 10
+    assert cached_model.total_bytes() == linear_numel * 4 * 2
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_cur_vram_bytes(device: str):
+    model = DummyModule()
+    # Model starts in CPU memory.
+    cached_model = CachedModelWithPartialLoad(model=model, compute_device=torch.device(device))
+    assert cached_model.cur_vram_bytes() == 0
+
+    # Full load the model into VRAM.
+    cached_model.full_load_to_vram()
+    assert cached_model.cur_vram_bytes() > 0
+    assert cached_model.cur_vram_bytes() == cached_model.total_bytes()
+    assert all(p.device.type == device for p in model.parameters())
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_partial_load(device: str):
+    model = DummyModule()
+    # Model starts in CPU memory.
+    cached_model = CachedModelWithPartialLoad(model=model, compute_device=torch.device(device))
+    model_total_bytes = cached_model.total_bytes()
+    assert cached_model.cur_vram_bytes() == 0
+
+    # Partially load the model into VRAM.
+    target_vram_bytes = int(model_total_bytes * 0.6)
+    loaded_bytes = cached_model.partial_load_to_vram(target_vram_bytes)
+    assert loaded_bytes > 0
+    assert loaded_bytes < model_total_bytes
+    assert loaded_bytes == cached_model.cur_vram_bytes()
+    assert loaded_bytes == sum(calc_tensor_size(p) for p in model.parameters() if p.device.type == device)
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_partial_unload(device: str):
+    model = DummyModule()
+    # Model starts in CPU memory.
+    cached_model = CachedModelWithPartialLoad(model=model, compute_device=torch.device(device))
+    model_total_bytes = cached_model.total_bytes()
+    assert cached_model.cur_vram_bytes() == 0
+
+    # Full load the model into VRAM.
+    cached_model.full_load_to_vram()
+    assert cached_model.cur_vram_bytes() == model_total_bytes
+
+    # Partially unload the model from VRAM.
+    bytes_to_free = int(model_total_bytes * 0.4)
+    freed_bytes = cached_model.partial_unload_from_vram(bytes_to_free)
+    assert freed_bytes >= bytes_to_free
+    assert freed_bytes < model_total_bytes
+    assert freed_bytes == model_total_bytes - cached_model.cur_vram_bytes()
+    assert freed_bytes == sum(calc_tensor_size(p) for p in model.parameters() if p.device.type == "cpu")
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_full_load(device: str):
+    model = DummyModule()
+    cached_model = CachedModelWithPartialLoad(model=model, compute_device=torch.device(device))
+
+    # Model starts in CPU memory.
+    model_total_bytes = cached_model.total_bytes()
+    assert cached_model.cur_vram_bytes() == 0
+
+    # Full load the model into VRAM.
+    loaded_bytes = cached_model.full_load_to_vram()
+    assert loaded_bytes > 0
+    assert loaded_bytes == model_total_bytes
+    assert loaded_bytes == cached_model.cur_vram_bytes()
+    assert all(p.device.type == device for p in model.parameters())
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_full_load_from_partial(device: str):
+    model = DummyModule()
+    cached_model = CachedModelWithPartialLoad(model=model, compute_device=torch.device(device))
+
+    # Model starts in CPU memory.
+    model_total_bytes = cached_model.total_bytes()
+    assert cached_model.cur_vram_bytes() == 0
+
+    # Partially load the model into VRAM.
+    target_vram_bytes = int(model_total_bytes * 0.6)
+    loaded_bytes = cached_model.partial_load_to_vram(target_vram_bytes)
+    assert loaded_bytes > 0
+    assert loaded_bytes < model_total_bytes
+    assert loaded_bytes == cached_model.cur_vram_bytes()
+
+    # Full load the rest of the model into VRAM.
+    loaded_bytes_2 = cached_model.full_load_to_vram()
+    assert loaded_bytes_2 > 0
+    assert loaded_bytes_2 < model_total_bytes
+    assert loaded_bytes + loaded_bytes_2 == cached_model.cur_vram_bytes()
+    assert loaded_bytes + loaded_bytes_2 == model_total_bytes
+    assert all(p.device.type == device for p in model.parameters())
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_full_unload_from_partial(device: str):
+    model = DummyModule()
+    cached_model = CachedModelWithPartialLoad(model=model, compute_device=torch.device(device))
+
+    # Model starts in CPU memory.
+    model_total_bytes = cached_model.total_bytes()
+    assert cached_model.cur_vram_bytes() == 0
+
+    # Partially load the model into VRAM.
+    target_vram_bytes = int(model_total_bytes * 0.6)
+    loaded_bytes = cached_model.partial_load_to_vram(target_vram_bytes)
+    assert loaded_bytes > 0
+    assert loaded_bytes < model_total_bytes
+    assert loaded_bytes == cached_model.cur_vram_bytes()
+
+    # Full unload the model from VRAM.
+    unloaded_bytes = cached_model.full_unload_from_vram()
+    assert unloaded_bytes > 0
+    assert unloaded_bytes == loaded_bytes
+    assert cached_model.cur_vram_bytes() == 0
+    assert all(p.device.type == "cpu" for p in model.parameters())
+
+
+@parameterize_mps_and_cuda
+def test_cached_model_get_cpu_state_dict(device: str):
+    model = DummyModule()
+    cached_model = CachedModelWithPartialLoad(model=model, compute_device=torch.device(device))
+
+    # Model starts in CPU memory.
+    assert cached_model.cur_vram_bytes() == 0
+
+    # The CPU state dict can be accessed and has the expected properties.
+    cpu_state_dict = cached_model.get_cpu_state_dict()
+    assert cpu_state_dict is not None
+    assert len(cpu_state_dict) == len(model.state_dict())
+    assert all(p.device.type == "cpu" for p in cpu_state_dict.values())
+
+    # Full load the model into VRAM.
+    cached_model.full_load_to_vram()
+    assert cached_model.cur_vram_bytes() == cached_model.total_bytes()
+
+    # The CPU state dict is still available, and still on the CPU.
+    cpu_state_dict = cached_model.get_cpu_state_dict()
+    assert cpu_state_dict is not None
+    assert len(cpu_state_dict) == len(model.state_dict())
+    assert all(p.device.type == "cpu" for p in cpu_state_dict.values())

tests/backend/model_manager/load/model_cache/dummy_module.py

@@ -0,0 +1,13 @@
+import torch
+
+
+class DummyModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear1 = torch.nn.Linear(10, 10)
+        self.linear2 = torch.nn.Linear(10, 10)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.linear1(x)
+        x = self.linear2(x)
+        return x

tests/backend/model_manager/load/model_cache/test_torch_function_autocast_context.py

@@ -0,0 +1,50 @@
+import pytest
+import torch
+
+from invokeai.backend.model_manager.load.model_cache.torch_function_autocast_context import (
+    TorchFunctionAutocastDeviceContext,
+    add_autocast_to_module_forward,
+)
+from tests.backend.model_manager.load.model_cache.dummy_module import DummyModule
+
+
+def test_torch_function_autocast_device_context():
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA is not available.")
+
+    model = DummyModule()
+    # Model parameters should start off on the CPU.
+    assert all(p.device.type == "cpu" for p in model.parameters())
+
+    with TorchFunctionAutocastDeviceContext(to_device=torch.device("cuda")):
+        x = torch.randn(10, 10, device="cuda")
+        y = model(x)
+
+    # The model output should be on the GPU.
+    assert y.device.type == "cuda"
+
+    # The model parameters should still be on the CPU.
+    assert all(p.device.type == "cpu" for p in model.parameters())
+
+
+def test_add_autocast_to_module_forward():
+    model = DummyModule()
+    assert all(p.device.type == "cpu" for p in model.parameters())
+
+    add_autocast_to_module_forward(model, torch.device("cuda"))
+    # After adding autocast, the model parameters should still be on the CPU.
+    assert all(p.device.type == "cpu" for p in model.parameters())
+
+    x = torch.randn(10, 10, device="cuda")
+    y = model(x)
+
+    # The model output should be on the GPU.
+    assert y.device.type == "cuda"
+
+    # The model parameters should still be on the CPU.
+    assert all(p.device.type == "cpu" for p in model.parameters())
+
+    # The autocast context should automatically be disabled after the model forward call completes.
+    # So, attempting to perform an operation with comflicting devices should raise an error.
+    with pytest.raises(RuntimeError):
+        _ = torch.randn(10, device="cuda") * torch.randn(10, device="cpu")

tests/backend/model_manager/model_manager_fixtures.py

@@ -25,7 +25,7 @@ from invokeai.backend.model_manager.config import (
     ModelVariantType,
     VAEDiffusersConfig,
 )
-from invokeai.backend.model_manager.load import ModelCache
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.util.logging import InvokeAILogger
 from tests.backend.model_manager.model_metadata.metadata_examples import (
     HFTestLoraMetadata,